Crimeless

A Prediction Tool for Law Enforcement

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Our Team


The Creators of Crimeless

...

Dev Rishi

Senior

Pforzheimer House

...

Carl Fernandes

Senior

Eliot House

...

James Ruben

Senior

Dunster House

...

Wilder Wohns

Senior

Pforzheimer House

Mission


Heatmap of Crime in Boston




Who We Are




We are a group of 4 seniors at Harvard College with a strong interest in using data science to solve real-world problems.

What We Hope to Achieve




To date, “Big Data” and Data Analysis have helped corporations become more cost-effective and politicians reach voters in smarter ways. Unfortunately, despite the ubiquity of publicly available data, there haven’t been large-scale or successful attempts at helping Law Enforcement police the streets more effectively, and those attempts that have been made are often on expensive platforms that are not readily accessible to the general public.

What we seek to do in this project is build out data analytics models for Law Enforcement that can demonstrate the power of data analysis effectively enough so as to encourage more work on this topic and to keep our streets safe.

Development


The Making of Crimeless

The first part of our project is our Static Analysis. This part of our project consists of an exploratory study of crime in Boston and an analysis of which static factors (i.e. factors that do not change on an hourly or daily basis) are most likely to lead to a more crime-prone area.


The second part of our project is our Dynamic Analysis. This part of the project consists of predicting when and where a crime will occur by using dynamic factors (things that change frequently over time such as weather, time of day, public holidays or weekends etc) to determine if a crime will occur or not in a given location at a given time. We then use the accuracy of our predictions and a probability of success at stopping a predicted crime to estimate an estimated positive economic impact on Boston.



Analysis




Our project attempts to predict whether or not a crime will occur in a given location at a given time based on a set of dynamic factors in conjunction with static factors that we processed earlier. Ultimately, this type of data could be incredibly helpful for police in order to dynamically allocate their patrols to the most threat-prone areas based on the changing variables of the day. Based our analysis, we were able to find out that the Random Forest Classifier optimized over our training dat does a fantastic job at saving the Boston Police Department money. There are a number of considerations that one should look into before "blindly" accepting our results -- it may be a an oversimplification to say that the cost of additional police is just the additional hourly wage paid to the officers, and it may also be an optimistic oversimplification to say that if cops know when/where a crime is going to occur, they can prevent it with the probabilities discussed above. However, none the less, we feel that the vast majority of our assumptions were grounded in intensive research and are fair to be considered over a vast range of potential scenarios. At the end of the day, the number we get of saving the Boston Police Department a potential of 67 million dollars over 3 and a half years is something we believe at least indicates that this is a direction the department could benefit from exploring further and hopefully invites future work into the space.

Future Work


Contact Us


Questions about Crimeless?

Harvard College
Cambridge, MA 02138

carlfernandes@college.harvard.edu
jruben@college.harvard.edu
awohns@college.harvard.edu
dev.rishi777@gmail.com

304-580-1392

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